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744,934 tools. Updated 2026-11-05 01:23

"A platform for finding maps or navigation tools by TomTom" matching MCP tools:

  • List a public/competitor creator's videos by platform + handle. Sort by 'recent' or 'top' (best-performing); optionally with analysis inline. Only returns creators already in the analysis library — for one you haven't ingested yet this returns reason="creator_not_in_library" with a next_step of analyze_creator(platform, username), not an error. When more videos exist beyond this page the response includes next_cursor — pass it back as cursor (same sort/filters) to fetch the next page; when next_cursor is absent you have everything.
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  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
    ConnectorNo auth
  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
    ConnectorNo auth
  • Leave feedback about AgentMarketplace itself — bugs, confusing tools, feature requests. This is for feedback about the platform, not about a counterparty. There is no reputation or rating system here — that lives in AgentTrust. Args: access_token: AgentAuth bearer token (requires ``market.read``). message: Free-text feedback, up to 2000 characters. Returns: ``{"recorded": true}`` on success.
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  • Look up a NUCC provider taxonomy code (the specialty codes used on HIPAA transactions + NPI registrations) or search by keyword. Returns classification + type + optional specialization. Source: NUCC Health Care Provider Taxonomy Code Set (public; CMS-accepted). WHEN TO USE: Resolving a NUCC provider taxonomy code to its specialty description. WHEN NOT: For finding a provider by specialty in a region (not yet implemented).
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Matching MCP Servers

Matching MCP Connectors

  • TomTom MCP — wraps the TomTom Search & Routing APIs (api.tomtom.com)

  • Put a map on a site with your AI: map your places, design the look, and get the code.

  • Returns the plain-language meaning and step-by-step fix for one Malinois finding. Use it while helping the user fix an issue reported by scan_app, or when they ask what a finding means. Pass the rule_id exactly as scan_app returned it. Read-only, no network, instant.
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  • Fetch the full content for one id returned by `search`: a rental listing (rental:<uuid>), a plan set (plan:<uuid>) or a published page (page:<route>). Returns {id, title, text, url, metadata}. Always cite the url. Hosts that can call specific tools should prefer get_rental or the resources.
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  • Fetch the full public detail for one AI tool by its listing slug (as returned by search_tools' toolUrl, e.g. '/tools/acme-writer' -> slug 'acme-writer'). Call this after search_tools to get a tool's full description, launch date, revenue signals (verified or self-reported), and for-sale status. Returns null if the slug doesn't resolve to a live Launchelion listing.
    ConnectorNo auth
  • Apply a post-hoc triage verdict to a finding that was previously recorded via record_finding. Used by the /reply-to-code-review flow to persist whether a finding is TRUE / FALSE / UNCERTAIN and the FIX / DEFER / IGNORE action taken. The finding is located only by the exact `finding_id` returned by list_pr_findings, on ANY lifecycle status (open, fixed, or dismissed — so verdicts still apply after a PR is merged and findings were swept to dismissed). Only findings recorded into the data layer (the aictrl-dev MCP path) can receive a verdict. Idempotent: re-applying overwrites the prior verdict. A not_found response is an application error; call list_pr_findings to refresh stale IDs and do not treat the write as successful. `judged_at_sha` is required: send the full 40-hex commit SHA of the code you actually read. A SHA that is neither the review SHA nor a later commit of the same PR is rejected with validation_error. `reasoning` is required too (#5364): a verdict whose reasoning cannot be audited later is not worth storing. Each write appends to the finding's judgment history; a re-judge from the same run at the same commit updates that run's own row rather than appending a duplicate. A FALSE verdict on an OPEN finding of severity MINOR or NIT (medium/low/info) also dismisses it as a false positive, with your `reason` as the note; on MAJOR or BLOCKER (high/critical) it stays a suggestion a person confirms. The result reports `autoDismissed` (and `autoDismissReason` when false). A dismissal is not reopened by this tool; a person can restore the finding from the pull-request page.
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    Destructive
    API key
  • Answer one LLM request of a job running on your model (every start tool's default unless use_platform_models=true or an explicit platform model): you are its model. Read the request under get_job_status(job_id).llm_requests (or in the start tool's return): run systemPrompt + messages with the listed tools and toolChoice on your own model, then pass the result here as response in MCP CreateMessageResult shape — {role: 'assistant', model: '<your model>', stopReason: 'toolUse' | 'endTurn', content: [{type: 'tool_use', id, name, input}] | {type: 'text', text}} — with optional usage {input_tokens, output_tokens}. toolChoice.mode 'required' means one tool_use block whose input matches the tool's inputSchema. The platform validates the answer like a provider's: a rejected answer returns as a new request with the error in a tool_result (isError) — fix and answer again. Returns the next pending request(s), the terminal result, or the running status after wait_seconds. Not for question pauses (answer_job_question). No platform LLM call. Guide: enricher://docs/llm-delegation.
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  • Retrieve a Lemma schema by its ID via GET /v1/schemas/{id}. A schema declares how documents of a given type are interpreted and normalized. Returns SchemaMeta { id, description? } with additionalProperties open — implementations commonly include a `normalize` artifact (WASM that maps raw documents to canonical form) and its content hash. Use this when you need to interpret attribute keys returned by lemma_query_verified_attributes.
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  • Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.
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  • [$0.005] Recently analysed tokens, each already carrying a verdict, to rank locally before paying for depth. Filters: hours (1-168), min_mcap, platform, limit (1-100). A time-ordered feed with no query; to find a token you know by name or mint, use search_tokens.
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  • Fetch the full public detail for one AI tool by its listing slug (as returned by search_tools' toolUrl, e.g. '/tools/acme-writer' -> slug 'acme-writer'). Call this after search_tools to get a tool's full description, launch date, revenue signals (verified or self-reported), and for-sale status. Returns null if the slug doesn't resolve to a live The AI Tools Index listing.
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  • High and low water times and heights at any coastal point, from the FES2022 global tide atlas: a list of events, each with type (high or low), time and height_m. Use it for when high or low tide is; for a continuous curve use get_tide_heights, and for tides together with sunrise, golden hour and moon phase use get_tide_window. Heights are metres relative to MEAN SEA LEVEL, not chart datum — never use for navigation. Defaults: from today, 2 days or the plan's limit if lower. Requires an API key on a paid Tides plan.
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  • Re-check a fix without a full scan. With opportunity_id, re-checks an inbox readiness task; the result updates the task. With check_id, re-checks that open finding on its affected pages. Returns pending with the re-check's id; follow it with get_operation or get_readiness_finding's check_request_id. Cost: by opportunity_id free, once every 10 minutes; by check_id one of the plan's monthly re-checks.
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  • One TikTok account: its follower count, its lifetime likes and how many videos it has posted. Use when: sizing a creator or a brand on TikTok, checking a handle is real, reading a bio before outreach. Not for: the account's videos, use tiktok_get_videos. Where its audience is, use tiktok_get_audience. Finding accounts by topic, use tiktok_search_videos and read the authors. Returns: Record. The account's handle, name, bio, verified, followers, following, posts_count, likes, views, website (the link in bio) and links, with other profile fields where TikTok fills them. null or empty = the platform does not publish it. Cost: 1 credit. Cached 24 hours. Example: {"handle": "@gymshark"}
    ConnectorAPI key
  • TikTok videos matching a keyword, ranked by the platform's own search. Use when: "what is TikTok saying about X", finding creators in a category, watching a product or a brand name. Not for: one account's own videos, use tiktok_get_videos. A complete window of everything posted (TikTok search is ranked and never complete). Returns: List, ranked. rows[]: videos with author and author_followers, created_at, text (cut at 2000 characters), likes, comments, shares, saves, views, media, duration_s, links, transcript and top_comments[]. saves is how many people saved it, music the sound, and author_followers sizes each creator without another call. null or empty = the platform does not publish it. Cost: 1 credit per page. Cached 6 hours. Example: {"query": "creatine gummies", "since": "month", "sort": "popular"}
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  • Search stored daily top-chart snapshot records from the iOS App Store and Google Play; omitting `date` selects the latest snapshot. Costs 1 credit per record returned (minimum 2), so keep `page_size` to what you need. Filter by app, store, platform, chart, country, or category; use datasets_apps_search for app records or datasets_apps_reviews_search for stored reviews.
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  • Search stored daily top-chart snapshot records from the iOS App Store and Google Play; omitting `date` selects the latest snapshot. Costs 1 credit per record returned (minimum 2), so keep `page_size` to what you need. Filter by app, store, platform, chart, country, or category; use datasets_apps_search for app records or datasets_apps_reviews_search for stored reviews.
    ConnectorNo auth